A framework for combining Genetic Algorithms with ILP methods is introduced and a novel binary representation and relevant genetic operators are discussed. It is shown that the proposed representation encodes a subsumption lattice in a complete and compact way. It is also shown that the proposed genetic operators are meaningful and can be interpreted in ILP terms such as lgg(least general gener...

A new mutual information based algorithm is introduced for term selection in spatio-temporal models. A generalised cross validation procedure is also introduced for model length determination and examples based on cellular automata, coupled map lattice and partial differential equations are described.

This paper addresses the multi-mode multi-skilled resource-constrained project scheduling problem. Activities of real world projects often require more than one skill to be accomplished. Besides, in many real-world situations, the resources are multi-skilled workforces. In presence of multi-skilled resources, it is required to determine the combination of workforces assigned to each activity. H...

optimization of structures for minimum weight has become important in the recent designs. in this study, a genetic optimization algorithm for weight minimization of steel frames has been used. the genetic algorithm is an optimization and search technique based on the principles of genetics and natural selection. constraints regarding material strength and serviceability are taken from “aisc cod...

in according to the simplicity of the lattice boltzmann method’s(lbm) algorithm and its benefits, it has been used as a successful method in computational fluid dynamics in the last decades. in this paper, lbm was used to simulate the flow over a cylinder. to analyze the application of lbm in simulation curved surface, different methods to compute drag coefficient were used. these methods are: ...

A route planning method based on gradient-field quantum genetic algorithm model was presented in this paper. It introduces the gradient field of a grid map to quantum genetic algorithm model and uses quantum genetic algorithm (QGA) to optimize the cost function of route planning. By combining the quantum characteristics with the capabilities of the large diversity of the population, as well as ...

running a genetic algorithm entails setting a number of parameter values. Finding settings that work well on one problem is not a trivial task and a genetic algorithm performance can be severely impacted. Moreover we know that in natural environments population sizes, reproduction and competition rates, change and tend to stabilise around appropriate values according to some environmental facto...